Influence of fat on the perceived texture of set acid milk gels: a sensory perspective

Food Hydrocolloids 20 (2006) 305–313 www.elsevier.com/locate/foodhyd

Influence of fat on the perceived texture of set acid milk gels: a sensory perspective Rogerio Pereiraa,1, Lara Matia-Merinoa, Veronika Jonesb, Harjinder Singha,* b

a Riddet Centre, Massey University, Private Bag 11 222, Palmerston North, New Zealand Fonterra Research Centre, Private Bag 11 029, Dairy Farm Rd, Palmerston North, New Zealand

Abstract Correlation of sensory perception of texture with instrumental parameters is key to understanding how specific structure formation can influence the consumer acceptability of a food product. Experimental acid milk gels with different solids-non-fat content (10–20%, w/v), with and without added fat (0–4% fat, w/v), were manufactured and characterised using quantitative descriptive analysis, confocal microscopy and small/large deformation rheology. Confocal micrographs of the gels showed that the gel structures were in agreement with the perceived sensory textural differences, in that the addition of fat, up to 4% (w/v), caused major changes in the microstructure of the network and in the overall perception of texture. This was observed mainly at low total solids levels (10–14%); no significant changes in microstructure or sensory perception of texture were detected at high total solids levels (above 18%), regardless of fat addition. The main effects of increasing fat content in the gels were a decrease in the mean pore size and an increase in the average cluster size. Added fat also caused the gels to become firmer, more resistant to penetration, more cohesive and sticky, creamier and less compressible before fracture (less ‘give’). Both instrumental and quantitative microstructural image analysis results correlated with perceived texture, and, when used in combination, these data sets generated an estimated model with satisfactory predictive ability for textural parameters as assessed by a trained panel (pred r2Z96.3%). q 2005 Elsevier Ltd. All rights reserved. Keywords: Acid milk gels; Sensory; Texture; Correlation

1. Introduction Understanding the relationship between the microstructure of food gels and their sensory quality is important for producing foods with the properties that consumers look for in a food choice situation. For milk-based gels, textural attributes can be as important as, or sometimes even more important than, flavour in determining a consumer’s acceptability of the product (Bourne, 2002). Therefore, characterising the texture is a crucial first step to defining how uniform in quality, and thus how successful, a specific product will be in comparison with similar products from which consumers can choose.

* Corresponding author. Tel.: C64 6 350 4401; fax: C64 6 350 5655. E-mail address: [email protected] (H. Singh). 1 Present Address: HortResearch, 120 Mt Albert Rd, Private Bag 92 169, Mt Albert, Auckland, New Zealand.

0268-005X/$ - see front matter q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodhyd.2005.01.009

Fundamentally, texture is a sensory attribute that we try to simulate and understand in the food laboratory using mostly physical methods. The texture of an acid milk gel (such as yoghurt), in particular, is created from the manner in which the constituent particles interact to form a continuous colloidal network or microstructure, with variations in dimensions and shapes of droplets, ˚ stro¨m, & Hermansprotein strands and pores (Langton, A son, 1997). The way in which this continuous network is perceived by the human senses will, ultimately, define the texture. Most of the published work regarding correlations between sensory perception and instrumental parameters has been based on rheological measurements, many of which vary considerably with the instrument used and/or the test principles and conditions (Bourne, 2002; Irigoyen, Castiella, Ordonez, Torre, & Ibanez, 2002; Meullenet, Lyon, Carpenter, & Lyon, 1998; Pereira, Singh, Munro, & Luckman, 2003; Ro¨nnega˚rd & Dejmek, 1993). Instrumental evaluation of texture has been reported to have advantages over sensory assessment, such as better reproducibility of

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results, speed of result generation and lower running costs, and has been recommended as a routine procedure when good correlation with sensory perception of texture can be demonstrated (Bourne, 2002; Skriver, Holstborg, & Qvist, 1999). The quality of the correlation between the sensory perception of texture and rheological measurements has been investigated (Bourne, 1983; Drake, Gerard, Truong, & Daubert, 1999; Hough et al., 1996; Pereira, Bennett, Hemar, & Campanella, 2001), but very few studies have attempted to establish correlations between the sensory perception of texture and structural data from image analysis of micrographs (Hullberg & Ballerini, 2003; Langton et al., 1997; Thybo, Szczypinski, Karlsson, Dønstrup, StødkildeJørgensen, & Andersen, 2004). Any physical or rheological response of a gel will be derived directly from its microstructure; therefore, it would be of interest to correlate the microstructure directly with sensory assessments. Texture characterisation at a microscopic level using image analysis has been reported, but only to a limited extent (de Bont, van Kempen, & Vreeker, 2002; Hullberg & Ballerini, 2003; Langton et al., 1997; Thybo, Bechmann, Martens, & Engelsen, 2000; Thybo, Szczypinski, Karlsson, Dønstrup, Stødkilde-Jørgensen and Andersen, 2004). Current advances in microscopy technology and image analysis software may prove to be valuable in allowing accurate modelling of perceived texture as a function of microstructure and, in turn, in engineering specific microstructures to deliver the desired perception of texture that consumers seek. This study used a controlled experimental design and a range of texture characterisation techniques to illustrate the effect of fat addition, at different levels of total solids, on the formation of structure in acid milk gels made from reconstituted skim milk. In addition, attempts were made to model correlations between sensory data and instrumental data to describe how structural changes influence sensory perception. Predictive models that used instrumental parameters for correlation with perceived texture were also developed.

2. Materials and methods 2.1. Preparation of acid milk gels Low heat skim milk powder (Fonterra Co-operative Group Ltd., New Zealand) was reconstituted in demineralised water to protein concentrations ranging from 3.3 to 6. 6% (w/v). Frozen fat for milk recombination (FFMR) (Fonterra Co-operative Group Ltd., New Zealand) was added to some of the milks at concentrations of 2 and 4% (w/v). The reconstituted milks were heated to 90 8C in a pilot-scale indirect ultra high temperature (UHT) plant (Alfa Laval, Australia), and were then transferred at that temperature to stainless steel beakers placed in a

thermostatically controlled water bath and held at 90 8C for 15 min under agitation. Rapid cooling of the milks to 20–23 8C was achieved using ice water. The mean fat droplet size distribution (d32 or volume–surface average diameter) was measured using a Mastersizer MSE static laser light-scattering analyser (Malvern, Worcestershire, UK). The parameters used to analyze the particle size were defined by the presentation code 2NAD. The milks were subsequently acidified at 35 8C over a period of 15 h, using a mother culture prepared with a freeze-dried mixture of Lactococcus delbrueckii subsp. bulgaricus and Streptococcus salivarius subsp. thermophilus (Joghurt 709, VISBYVAC Series 1000, Danisco, Niebull, Germany). The milks were inoculated with 2% (w/w) mother culture prior to incubation at 35 8C. The experimental design used allowed for the manufacture of 18 different gel samples. The final pH of the gels was found to range between 3.9 and 4.2. 2.2. Sensory evaluation Sensory evaluation was carried out with a trained panel of 10 panelists, using both oral and non-oral attributes. Attributes evaluated orally were: firmness, thickness, adhesiveness, creaminess, coarseness and dissolvability. Non-oral attributes were: firmness, give, resistance to penetration, cohesiveness of mass, adhesiveness to spoon, moisture release in-hand, adhesiveness and breakdown consistency. Definitions for each of these attributes are given in Table 1. Non-oral and oral evaluations were carried out using separate containers, independently coded and randomised, to avoid bias. Samples were presented to the panelists in individual, three-digit-coded plastic containers (50 ml) placed in cooled metal blocks to keep the gel temperatures low and uniform during testing. The gel temperature during testing was 7G0.8 8C and evaluation was done on 3- to 5-day-old gels that were kept under refrigeration after the incubation period. Testing was conducted under white light conditions. Each panelist evaluated each of the 18 different gels six times (three times orally, three times non-orally) over nine sessions. They evaluated 12 gels in each session. On each test day, the presentation order was randomly assigned by computer (C5, Compusense Inc., Guelph, Ontario, Canada). Evaluations were made using a 150-mm line scale anchored with references for each of the tested attributes. The references used were the softest (10% total solids, no added fat) and the firmest (24% total solids, 4% added fat) gels in the range studied. 2.3. Instrumental measurements 2.3.1. Compression The mechanical properties of 3-day-old gels were evaluated at 6–7 8C using a pseudo-compression (‘back extrusion’) test on a TA-XT2 Texture Analyser (Stable

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307

Table 1 Definitions of the sensory textural attributes (non-oral and oral) of acid milk gels Sensory attribute

Definition

Firmness

Force required to compress the gel, before fracture and permanent structural damage, using the middle or index finger Extent to which the gel can be compressed before fracture and permanent structural damage Extent of resistance offered by the gel to penetration with a plastic spoon, using a constant speed, down to the bottom of the container Degree to which the gel holds together when scooped up with a plastic spoon Ease of removal/slipping off of the gel from a plastic spoon when the spoon is slowly tipped at an angle of 458 Amount of liquid observed around the gel edges when a spoonful of the gel sample is placed in the hand (palm) Lumpy (0) to mushy (150) consistency of the gel after being squashed three times, in orthogonal directions, between the thumb, and index and middle fingers Amount of residual gel stuck on to the fingers after squishing it for evaluation of breakdown consistency Force required to compress a sample of the gel between the tongue and the palate Density or consistency of the gel after initial compression between the tongue and the palate Amount of gel stuck to the tongue or palate after compression of the sample Presence of coarse particles in the mouth (felt with tongue) after initial compression Combined sensation of smoothness and thickness of the gel, associated with a fatty mouthcoating experience Ease with which a sample of the gel dissolves in the mouth

Give Resistance Cohesiveness of mass Adhesiveness to spoon Moisture release Breakdown consistency Adhesiveness in hand Firmness (mouth) Thickness (mouth) Adhesiveness (mouth) Coarseness (mouth) Creaminess (mouth) Dissolvability (mouth)

Micro Systems, Surrey, England). Gel samples, 40 mm deep, were compressed using a flat aluminium upper plate, 15 mm in diameter, at a crosshead speed of 10 mm/s and to a depth of 5 mm. Curves of force versus time were analysed using Microcal Origin version 5 (Microcal Software Inc., Northampton, USA) to generate rheological parameters for subsequent correlation analysis. Five rheological parameters were derived: maximum force in compression (Fmax), deformation to peak force (TFmax), work in compression (Area C), work in decompression (Area K) and slope of the force versus time curve (Slope). All measurements were made in quadruplicate. 2.3.2. Frequency sweep Gels were prepared using 10 ml of cultured milk in 60mm diameter Petri dishes (Biolab Scientific, Auckland, New Zealand) and the evaluation was performed on 3-dayold gels at 20 8C. The equipment used for the measurements was a Rheometrics SR-5000 (Rheometric Scientific, New Jersey, USA) and a special plate/dish holder was built for fixing the Petri dishes during the test. The parallel plate geometry was used (upper plate Z40 mm diameter, strain Z1%, frequency range Z0.01–4 Hz). Any visible whey on the surface of the gels was carefully removed with a micropipette prior to running the test. G 0 , G 00 , G* and tan d were the measured parameters. 2.3.3. Syneresis Syneresis was evaluated using 100 ml grade A glass volumetric flasks (Fortunaw NS 12.5/21, Germany) according to the procedure described by Lucey, Teo, Munro, and Singh, (1998c). A volume of 85 ml was added to each flask so that they were filled to just below the base of the neck. The gels were kept sealed at 4–6 8C for up to 3 days after gelation and measurements were made 0, 6, 12, 24 and 48 h after the end of the incubation period.

2.4. Microstructure Confocal laser scanning microscopy has been described as a useful technique for studying the formation of microstructure in yoghurt (Hassan, Frank, Farmer, Schmidt, & Shalabi, 1995) and was used in this study for microstructural analysis of the experimental acid milk gels. The sample preparation method using the fluorescent dye Fast Green FCF (Merck, Darmstadt, Germany) for proteins has been described previously (Lucey et al., 1998c). Nile Blue (BDH, Poole, England) was used for staining the fat droplets. The gels were examined on a Leica DM RBE confocal microscope LS 510 (Leica Lasertechnik GmbH, Heidelberg, Germany) with a 100x oil immersion objective (numerical aperture 1.4) and an Ar/Kr mixed gas laser source using excitation wavelengths of 568 and 488 nm for Fast Green and Nile Blue respectively. Experiments were done in duplicate, with 12 images captured per experiment. Image analysis was carried out using Image-Pro Plus, version 4.5 for Windows (Media Cybernetics, Silver Spring, USA), and five parameters were quantified: mean cluster size, mean cluster numbers, mean end point number (number of unconnected protein branches of a protein cluster), mean pore size and mean pore numbers. 2.5. Statistical analysis Statistical analysis was performed using MINITABw release 14 (Minitab Inc., State College, USA). The results were analysed using analysis of variance (ANOVA) and Tukey’s HSD significance test, principal component analysis (PCA) and principal component regression (PCR), as well as partial least squares (PLS) regression. Canonical correlation was performed using SAS version 8 (SAS Institute Inc., Cary, North Carolina, USA), using PROC GLM and PROC CANCORR.

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Table 2 Analysis of variance for individual sensory textural attributes of acid milk gels Model

Firmness (hand) Give

Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F Judge (J) Solids-Non-Fat (SNF) Fat (F) SNF*F

Resistance Cohesiveness of mass Adhesiveness to spoon Moisture release Breakdown consistency Adhesiveness in hand Firmness (mouth) Thickness (mouth) Adhesiveness (mouth) Coarseness (mouth) Creaminess (mouth) Dissolvability (mouth)

Mean squares

F-value

p-value

1434.33 170253.52 17426.10 2512.61

2.75 326.03 33.37 4.81

!0.01!0.001!0.001!0.001

6181.84 34241.32 60584.19 9787.14

7.41 41.07 72.66 11.74

!0.001!0.001!0.001!0.001

1383.56 161235.07 24146.14 1504.74

4.24 494.38 74.04 4.61

!0.001!0.001!0.001!0.001

906.63 158525.30 18617.16 3811.30

2.41 421.84 49.54 10.14

!0.05!0.001!0.001!0.001

3329.87 172867.80 33561.80 2179.46

8.54 443.27 86.06 5.59

!0.001!0.001!0.001!0.001

5753.42 158027.15 24088.83 2660.57

12.85 352.82 53.78 5.94

!0.001!0.001!0.001!0.001

2389.44 164294.75 30997.64 2266.13

8.00 549.89 103.75 7.58

!0.001!0.001!0.001!0.001

2940.90 114892.70 29196.79 1641.04

10.08 393.94 100.11 5.63

!0.001!0.001!0.001!0.001

4114.68 165950.61 20049.50 599.22

12.77 514.87 62.20 1.86

!0.001!0.001!0.001!0.05

3197.19 159308.57 22840.88 876.09

10.57 526.58 75.50 2.90

!0.001!0.001!0.001!0.01

5397.39 146902.78 21962.17 875.50

16.70 454.43 67.94 2.71

!0.001!0.001!0.001!0.01

21294.60 86886.95 2359.63 1304.72

30.77 125.53 3.41 1.89

!0.001!0.001!0.05!0.05

19183.24 107939.91 45967.85 1866.85

32.57 183.24 78.04 3.17

!0.001!0.001!0.001!0.001

4160.99 162767.73 22320.77 1197.43

7.67 299.89 41.12 2.21

!0.001!0.001!0.001!0.05

3. Results and discussion 3.1. Sensory evaluation Previous studies investigating the influence of protein content and fat content on the structural characteristics of stirred acidified milk gels (Schkoda, Hechler, & Hinrichs, 2001a,b) have shown that, whereas the concentration of casein governs the structural properties of the final product, fat addition prior to acidification seems to improve the serum-holding capacity of these products, when assessed instrumentally. Little is known about how these effects on the structural properties of the acidified gels are perceived using the human senses, and how closely sensory perception can be correlated with microstructural information about the gels. The sensory evaluation scores from the panel were analysed using ANOVA. The model used included effects of session, panelists, solids-non-fat (SNF) content, fat content and SNF-fat interaction. Mean squares and probability values for each textural attribute are shown in Table 2. For all textural attributes, both SNF content and fat content played a significant role in producing perceivable differences between the gels. Overall, however, the effect of SNF in producing sensory differences between the gels in this study was predominant, as shown by the mean square values in Table 2. ‘Give’ was the only textural attribute that

was primarily influenced by the fat content of the gel samples. The significant differences between the panelists for all descriptors were not entirely unexpected, especially because of the narrow textural range of the gels explored in the study. Although the training of panelists seeks to reduce this inherent variability, significance of the effect of panelists in sensory data analysis has been reported previously (Pereira et al., 2003). All interactions of the panelists with sessions, samples and replicates were included in the error term of the ANOVA, maximising the term against which all other give

10H0

sensory PC2 (3.2%)

Attribute

1

12H0 coarse mouth

16H0

0

moisture breakdown

dissolvability

firm mouth 20H2 adhesive mouth 20H020H4 thick mouth resistance 18H2 18H4 firm hand adhesive spoon 16H2 18H0 cohesive hand cream mouth adhesive hand 16H4

14H0 12H2

10H2

14H2 14H4 12H4

–1

10H4

–5

0 sensory PC1 (93.8%)

5

Fig. 1. Principal component plot for the Sensory textural attributes (for gel samples: initial numberZ% SNF, HZHeat-treated milk, final numberZ% Fat).

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were homogenised prior to acidification, the homogenised fat globules with adsorbed caseins would be expected to contribute actively to the formation of the gel network and to the gel properties (Schkoda et al., 2001b). All the instrumental measurements in this study were made after storage for 3 days, to match with the sensory assessments. As one example, the effects of fat content and total solids content on compression strength are shown in Fig. 2. As expected, an increasing level of total solids increased the compression strength (Fmax) of the gels. For the same level of SNF, only the high level of fat addition (4%) increased Fmax significantly (Fig. 2(b)), whereas Fmax of the gels decreased with increasing fat content at a given total solids content (Fig. 2(a)). Previous studies on dairy gels have shown that addition of emulsified fat to skim milk powder networks improves the mechanical properties of milk gels (Aguilera & Kessler, 1989; Aguilera, Kinsella, Liboff, Dickinson, Morr, & Xiong, 1993), as also found in this study, especially at added fat levels S4%. However, partial replacement of SNF by fat seems to change the compression properties of the gels by reducing the force needed to fracture, with fat possible acting as a lubricant. The instrumental principal component plot (Fig. 3) shows that, in this study, instrumental results (from (a) 1.6 1.4 1.2 Fmax (N)

effects were tested. Table 2 shows that, over and above the ‘noise’ or variability inherent to the panelists over sessions, samples and replicates, significant differences could still be detected between the many gels evaluated. PCA of the sensory results (Fig. 1) shows that, as the SNF content increased from 10 to 20% (w/v), the gels became firmer, more resistant to cutting, more adhesive or sticky on the hands and palate, more cohesive, coarse and thick, less prone to whey separation, less dissolvable in the mouth and more brittle under small applied stresses, regardless of the amount of fat added to the samples. Within each level of fat addition (0, 2 and 4%, w/v), differences between the experimental gels were more easily detectable at lower SNF levels, the gels being increasingly perceived as similar in texture as the total solids content was increased above 18%. Another observation from Fig. 1 is that ‘give’ was the major sensory attribute that differentiated gels with and without added fat. Samples without added fat were perceived as being more compressible than those to which fat was added, which was probably the result of the stronger gel network formed in the gels to which fat had been added. Similarly, differences between the gels were more pronounced when the total solids content was below 18%. Both oral and non-oral sensory attributes were effective at discriminating between the textures of the gels, with a high overall interrelationship of the attributes (Pearson correlation coefficient, rO0.9). ‘Give’ was, as expected, the one non-oral attribute that could be only marginally correlated with the oral subset. The possibility of using either oral or non-oral assessments in characterising the texture of acid gels has already been shown for skim milk gels (Pereira, Singh, Jones, & Munro, 2004). Confirmation of the correlation between the two data subsets for gels to which fat was added supports the use of non-oral attributes as a routine method for characterising gel texture, with the advantage of minimising oral and mental fatigue of panelists.

309

1 0.8 0.6 0.4 0.2 0

8

10

12

3.2. Instrumental characterisation and image analysis

14 16 Total solids (%)

18

20

22

(b) 1.6 1.4 1.2 Fmax (N)

In this study, the experimental gels were subjected to a range of different instrumental methods of texture characterisation, all of which were analysed using PCA. Amongst other factors, the textural properties of protein-based gels are influenced by the droplet size of the lipid filler (Mor, Shoemaker, & Rosenberg, 1999). Analysis of the fat droplet size distributions of the recombined milks, prior to gelation, revealed that all samples exhibited similar average volumeto-surface droplet diameters (d32), ranging from 0.46 to 0.52 mm, at all levels of skim milk powder and fat used. As all samples were heat treated at 90 8C, it was expected that denatured whey proteins would associate with casein micelles via intermolecular disulphide bonds, participating in the gel matrix and improving the rheological properties of the gels, as has been reported previously (Lucey, Tamehana, Singh, & Munro, 1998b). As all the fat-containing systems

1 0.8 0.6 0.4 0.2 0

8

10

12

14 SNF (%)

16

18

20

Fig. 2. Maximum force in compression (Fmax) of acid milk gels plotted as a function of (a) total solids level (TS) or (b) solid-non-fat level (SNF) at different fat contents 0%(B), 2%(-) or 4%(6).

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2 instrumental PC 2 ( 16.5%)

10H2

10H4

Area -

18H2 20H2 18H4

12H4 14H4

1

t an delta

16H4 12H0 12H2

0 –1

20H4

16H2 18H0

10H0

G" G'

TFmax

14H0

–2

syn 12 syn 24 syn 48 syn 6 syn 0

–3

Slope Fmax Area +

20H0 14H2

16H0

–4

–3

–2

–1

0

1

2

3

4

5

6

instrumental PC 1 (53.9%) Fig. 3. Principal component plot for the Instrumental textural attributes (for gel samples, initial numberZ% SNF, HZHeat-treated milk, final numberZ% Fat).

syneresis measurements, small deformation rheology and compression tests) were generally not as effective as the sensory panel in discriminating between the textures of the gels. Samples with higher total solids content, above 18%, were discriminated from those with lower total solids in terms of their more elastic behaviour under compression and in a frequency sweep test. In general, experimental gels without added fat and the group in which fat was added at

the 4% level could be well discriminated, with the latter group showing very little whey separation and higher tan d. In contrast, gels with the intermediate level of fat addition showed considerable variability when tested instrumentally. Confocal micrographs were used both qualitatively and quantitatively for correlation with the sensory results obtained from the trained panel. The correlation between the first principal components of the instrumental and microstructural data was found to be significant (p!0.001), but without a relatively high correlation coefficient (rZ 0.69). This could have been caused by the variability in the microstructural results, which is highly dependent on the number of replicates and the magnification used to obtain the micrographs. During acidification of milk, casein particles and denatured whey proteins associated with the surfaces of micelles aggregate into chains and clusters that are linked together to form a three-dimensional network (Bremer, Bijsterbosch, Schrijvers, van Vliet, & Walstra, 1990; Lucey et al., 1998c). Previously, scanning electron microscopy has revealed similar microstructure (porosity of recombined yoghurt protein matrices) with or without the inclusion of 1.5% fat (Barrantes, Tamime, Sword, Muir, & Kalab, 1996). It was suggested that the fat globules could become hidden within the protein clusters and chains of the matrix. However, transmission electron microscopy made it

Fig. 4. Confocal micrographs of acid milk gels at different levels of SNF (10–20%) where protein is replaced by fat (0–4%). Scale barZ10 mm.

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possible to visualise the fat globules, showing their interaction with casein micelles and their participation in the formation of the gel matrices (Barrantes et al., 1996). Fig. 4 shows representative confocal micrographs of the microstructures of acid gels with 14 and 20% total solids and containing different proportions of SNF and fat. In agreement with previous observations (Aguilera & Kinsella, 1991; Lucey, Munro, & Singh, 1998a), in gels containing fat, the recombined fat globules were embedded and fully connected into the matrix. The presence of fat caused major microstructural changes at a relatively low level of total solids (14%), whereas no significant changes were detected at a high total solids level (20%). These major changes resulted in a denser and less open microstructure, with a smaller pore size and increased ‘interconnectivity’ of the network. The same effect was detected when the SNF level was fixed and fat was incorporated into the network (Fig. 5). The average mean cluster size and the average mean pore size measurements at 10% SNF illustrated the microstructural changes that occurred with increasing fat (or increasing number of interacting particles) (Fig. 5(a)), as opposed to the relatively constant values obtained at 20% SNF (Fig. 5(b)). This correlates well with the findings from the sensory panel, who perceived the samples at high total solids levels (O18%) as having similar textural properties, regardless of the fat content (Fig. 1).

Mean cluster size (µm2)

100

10

10

1

1

Mean pore size (µm2)

100

(a) 1000

3.3. Statistical correlation Canonical correlation has been used to investigate the relationship between the sensory and instrumental and/or microstructural sets of data, in a procedure that maximises the correlation coefficients without assumptions about dependences of one set on another (MacFie & Hedderley, 1993) or necessarily a cause-effect relation between the data sets (Dijksterhuis, 1995). For predictive purposes, however, PLS regression (Martens & Martens, 1986) has been widely recommended because of the inherent testing for predictive performance by calculation of a cross-validated r2, using leave-one-out procedures (MacFie & Hedderley, 1993). Although both techniques were used for analysis of the data in this study, the outputs of the canonical correlation analysis are not presented; instead, models derived from PLS regression are presented. Because of the strong interrelationship between the variables within each data set in this study, as identified through principal component analysis, PLS regression was performed using the principal components themselves as the independent variables or predictors, and the sensory principal component as the response to be predicted. This procedure was a modified form of PCR, in which the leaveone-out cross-validation commonly used in PLS was applied. The analysis showed that when regressing the first sensory principal component (SPC1) against the principal components of the combined instrumental and microstructural data (XPC1, XPC2, XPC3), an estimated model with high predictive ability (pred r2Z96.3%) was generated. This means that it is possible to predict, with good accuracy, new observations for perceived sensory results from data gathered from confocal micrographs and instrumental texture analysis. Fig. 6 shows the model, based on

0.1 0

1

2

3

4

PLS Response Plot for SPC1 SPC1= 0.9832 XPC1+ 0.0761 XPC2 + 0.0428 XPC3

5

% fat

Calculated Response

1

100

Mean pore size (µm2)

Mean cluster size (µm2)

5.0

10

(b) 1000

311

2.5 0.0 –2.5 –5.0 Variable Fitted Crossval

–7.5 10

0

1

2

3

4

5

0.1

% fat Fig. 5. Mean cluster size (open symbols) and mean pore size (filled symbols) of acid gel microstructures, containing varying amounts of fat and (a) 10% SNF, (C,B) or (b) 20% SNF (:, 6).

–7.5

-5.0

-2.5 0.0 Actual Response

2.5

5.0

Fig. 6. PLS response plot for the first sensory principal component (SPC1), modelled as a function of instrumentalCmicrostructural data (XPC1, XPC2, XPC3). Pred r2Z96.3%.

312

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PLS Response Plot for GIVE GIVE = –07732 XPC1 - 0.1756 XPC2 - 0.3935 XPC3

Calculated Response

120 100

replicated measurements are produced, in order to generate a more accurate estimate of each parameter being quantified. This inherent variability could have contributed to the lack of good correlation results reported to date.

80 60

4. Conclusions

40

The present study showed how differences in the microstructure of acid milk gels, caused by differences in the SNF and fat contents, influenced the perception of the textural attributes of these products, and how image analysis could be used to model these changes in perception. Fat addition resulted in a decrease in the mean pore size and an increase in the mean cluster size. But these observations alone were not sufficient to predict, with a satisfactory degree of accuracy, the perceived texture of the gels. However, a combination of image analysis measurements with whey separation and rheological properties derived from a compression test enabled models to be estimated that had excellent predictive power for sensory textural attributes evaluated non-orally (firmness, moisture release, cohesiveness, adhesiveness, resistance to penetration) and orally (firmness, thickness, adhesiveness and dissolvability). Although this study was of a preliminary nature, advances in computer technology and software for image analysis may prove to be useful in generating more accurate and reproducible quantitative information from the gel microstructural fingerprints that correlate with the perception of texture and that can be used as a quality control tool for delivery of the sensory experience that consumers expect from milk gels.

20 Variable Fitted Crossval

0 0

20

40

60

80

100

120

Actual Response Fig. 7. PLS response plot for the sensory attribute ‘give’, modelled as a function of instrumentalCmicrostructural data (XPC1, XPC2, XPC3). Pred r2Z68.9%.

standardised coefficients and including fitted and crossvalidated fitted values. ‘Give’ is the one sensory attribute that could not be as well modelled as the other textural descriptors. Regression analysis showed that a model could be produced, with pred r2Z68.9%, which was still a reasonable predictive ability (Fig. 7). Canonical correlation analysis confirmed the good correlation found between the sensory data set and the group of instrumental and microstructural parameters (data not shown). However, no information on the predictive power of the model derived from this statistical procedure was available. PLS regression for SPC1 using only instrumental analysis resulted in a predictive model with pred r2Z 86.9%, with data generated from compression tests and gel permeability being those that more closely correlated with and predicted sensory response. Use of microstructural image analysis data only produced, in turn, a model with pred r2Z47.2%, which was quite a poor model for estimating perceived texture. The lack of published results showing good correlation between microstructure and perceived texture was identified previously by Langton et al. (1997), and was attributed to improperly defined experimental design. It is important to note that any minor lack of structural homogeneity of the samples tends to be highly magnified during image analysis, because of the small section of the sample that is being analysed and the high magnifications required for proper visualisation of structural information. To generate a good image of the texture to be quantified in image analysis, two assumptions need to hold: that significant variation in intensity levels between nearby pixels exists, and that homogeneity at some spatial scale larger than the resolution of the image occurs (Thybo et al., 2004). Large variability in image analysis results is therefore not uncommon, unless a large number of

Acknowledgements The authors acknowledge the assistance of the sensory panel at Fonterra Research Centre (Palmerston North). We would also like to thank Warwick Johnson, Liz Nickless, Thomas Huber, Stephen Hubbes, Raul Jacobsen Neto and Stephan Wullschleger for their valuable help in the manufacture and instrumental testing of the experimental gels. The financial support provided by the Fonterra Cooperative Group Ltd., New Zealand, is gratefully acknowledged.

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